Ranking data assets for processing natural language questions based on data stored across heterogeneous data sources

ABSTRACT

An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefits of U.S. Provisional Application No.62/901,196, filed on Sep. 16, 2019 and U.S. Provisional Application No.62/821,326, filed on Mar. 20, 2019, each of which is incorporated byreference in its entirety.

FIELD OF INVENTION

This disclosure concerns analysis of data stored across heterogenousdata sources in general and more specifically to ranking of data assetsacross heterogeneous data sources for answering natural languagequestions.

BACKGROUND

Organizations store data in multiple data sources, for example,relational databases, file systems, cloud storage, and so on.Furthermore, each type of data source may have multiple instances. Eachinstance of a data source may be provided by a different vendor. Forexample, the same organization may store some data in an ORACLE databaseand some data in SQLSERVER database. Each data source can be a complexsystem that requires an expert who can interact with the system.

Due to these complexities, any analysis of the data stored by theorganization becomes a complex project. This project may involve severalstages including (1) data discovery to identify what data is availablein each data source, (2) data import process to ingest data and move itto a storage system for analysis, (3) determination of the subset ofdata that is of interest, and (4) analysis by running queries andreviewing results for validation. Each stage can take weeks or monthsdepending on the complexity of the data stored. As a result, theorganization may take several weeks or months before any visibility isgained into the data stored. Furthermore, at the end of the process, adetermination may be made that the data that was necessary to answercertain questions was not available. Accordingly, the process may haveto be repeated to do further analysis of the data or to analyze the datafor other possible questions that may be of interest or to analyzedifferent subset of the data. This multiplies the entire delay by afactor depending on the number of times the process needs to berepeated.

Furthermore, this process is made more difficult since the users withhigh-level domain knowledge typically do not have the technicalexpertise to interact with the systems storing the data and the usersthat have expertise to interact with the systems typically lackhigh-level domain expertise. As a result, there is a knowledge gapbetween the two types of users that are interacting. For example, thelack of domain knowledge may cause the technical user to look forincorrect information and return a negative answer even if the requestedinformation as available. As a result, the domain experts are oftenunable to exploit the information available in the data sources to themaximum extent possible.

SUMMARY

An analysis system allows users to connect to a set of data sources andperform queries across the data sources. The data sources may be ofdifferent types, for example, a data source may be a relational databasesystem and another data source may be a file system. The analysis systemconnects with the data sources and retrieves metadata describing dataassets stored in each data source. For example, for a relationaldatabase system, the data assets may represent tables and for a filesystem, the data assets may be files. The analysis system receives anatural language question and generates execution plans for executingthem.

The analysis system receives a natural language question and identifiesphrases in the question. A phrase may comprise one or more keywords. Theanalysis system performs the following processing for each phrase. Theanalysis system identifies a plurality of data assets matching thephrase from the plurality of data sources. Each data asset is determinedto store data relevant for answering the question and is associated withthe phrase. For example, in an embodiment, the analysis system generatesa virtual data model comprising entities and relationships betweenentities. The analysis system associated a phrase with an entity andidentifies data assets that match the entity. The analysis systemdetermines a score for each data asset matching a phrase. The analysissystem determines the score based on factors comprising, a degree ofmatch between the phrase and metadata describing the data asset. Theanalysis system ranks the plurality of data assets based on their scoresand determines a subset of data assets based on the ranking. Theanalysis system sends the subset of data assets for display via a clientdevice.

The analysis system may determine the score for a data asset based onother factors. For example, each data asset may be associated with a setof questions previously received and the analysis system determines thescore for the data asset based on the number of distinct storedquestions for the data asset.

In an embodiment, the score for A data asset is determined based on aweighted aggregate of the stored questions for the data asset. eachstored question is weighted based on a number of times the storedquestion was invoked.

In an embodiment, the analysis system receives selections of a dataassets for one or more phrases. The analysis system generatesinstructions, for example, step by step directions describing how thedata required for processing the question will be accessed for theselected data assets from the data sources and processed. Theinstructions may comprise instructions to extract data of the selecteddata assets from the data sources, instructions to transform data tomatch formats across data sources, instructions to combine (e.g., join)data from different data sources, and so on. In some embodiments, theanalysis system publishes the instructions, for example, by sending theinstructions via an email to a system administrator or by displaying theinstructions via a user interface. In another embodiment, the analysissystem generates executable instructions, for example, SQL (structuredquery language) instructions. The analysis system receives a request toexecute the instructions and executes them using an execution engine togenerate results based on the received question.

Embodiments of a computer readable storage medium store instructions forperforming the steps of the above methods. Embodiments of the computersystem comprise one or more computer processors and a computer readablestorage medium that stores instructions for performing the steps of theabove methods.

The features and advantages described in this summary and the followingdetailed description are not all-inclusive. Many additional features andadvantages will be apparent to one of ordinary skill in the art in viewof the drawings, specification, and claims hereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an overall system environment for performing data model basedanalysis of data, in accordance with an embodiment.

FIG. 2 is the system architecture of an analysis system for performingdata model based analysis of data, in accordance with an embodiment.

FIG. 3A shows a user interface presenting a comprehensive view of alldata sources available in a system environment, according to anembodiment.

FIG. 3B shows a user interface for allowing a user to select specificdata assets to be processed in a data source, according to anembodiment.

FIG. 3C shows the user interface for receiving a search request andpresenting data assets that match the search request, according to anembodiment.

FIG. 3D shows the user interface for presenting associated questions fora search result, according to an embodiment.

FIG. 3E shows a user interface presenting a virtual data model for aquestion, according to an embodiment.

FIG. 3F shows the user interface for allowing a user to modify the dataasset corresponding to an entity of the virtual data model correspondingto a natural language question, according to an embodiment.

FIG. 3G shows a user interface for allowing a user to view lineage ofdata, according to an embodiment.

FIG. 4 is the overall process of collecting data used by the analysissystem, in accordance with an embodiment.

FIG. 5 shows a process of receiving and processing a natural languagequestion by generating a virtual data model, specific to the receivednatural language question, according to an embodiment.

FIG. 6 illustrates the process of mapping data stored in data sources tonatural language question, in accordance with an embodiment.

FIG. 7 is the process of ranking data assets for a question, accordingto an embodiment.

FIG. 8 is the process of processing questions using previously storedquestions, in accordance with an embodiment.

FIG. 9 is the process of performing preprocessing of data, in accordancewith an embodiment.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION

Conventional techniques require users to extract data from multiplesources to analyze and determine whether the data serves a specifiedpurpose, for example, whether it answers a given question. Conventionalsystems perform ETL (extract, transform, and load) process to extractall data from each data source to be able to join the data. Followingsteps are typically performed for answering a question based on datastored across a plurality of heterogeneous data sources: (1) Ask user toidentify each data source. (2) Perform ETL (extract transform load) jobto move all data from the data source. (3) Receive from users, a filterand determine a subset of the data obtained from each data source. (4)Receive from users, a query to join the subset of data from each datasource. (5) Generate a table storing the join result. (6) Execute thequery using the table, for example, using a BI (business intelligence)tool.

Embodiments of the analysis system allows users to ask questions withoutrequiring them to have technical knowledge of the underlying datasources. Embodiments of the invention eliminate the need to perform theETL process. Accordingly, the analysis system automatically performsseveral steps of the above process, for example, steps 1, 2, and 4. Theanalysis system automatically determines (1) what data needs to beprocessed, (2) where that data is present, and (3) how the data shouldbe extracted and combined.

FIG. 1 is an overall system environment for performing data model basedanalysis of data, in accordance with an embodiment. The systemenvironment comprises an analysis system 100, one or more data sourcesystems 110, one or more third party systems, and one or more clientdevices 130. The analysis system 100 obtains data and metadata fromvarious data source systems 110 and data models from third party systems120 and performs analysis of the data stored in the data source systemsusing the data models. The analysis system may present the analysis viaa user interface of the client device 130. The details of the analysissystem 100 are further described herein, for example, in FIG. 2 .

The data source systems 110 store data, for example, data used by anorganization or enterprise. A data source system 110 comprisesinstructions for processing data stored in the data source system 110and one or more data sources 125. A data source 125 of the data sourcesystem 110 has a data source type, for example, a relational database, afile system, a document oriented database system, and so on. As aresult, the system environment shown in FIG. 1 comprises the analysissystem 100 connected to a plurality of heterogenous data sources, eachdata source possibly having a distinct data source type.

The analysis system 100 executes the instructions of the data sourcesystem 110, for example, by invoking the API (application programminginterfaces) of the data source system 110 to access the data of the datasource 125. For example, a data source system 110 may be a databasemanagement system such that the data source is a database. The analysissystem 100 may execute API such as JDBC to access the data stored in adatabase management system. As another example, a data source system 110may be a file system, for example, HDFS (HADOOP file system) and thedata source refers to the files of the file system that store the data.

A particular type of data source may have multiple instances, forexample, instances of relational databases. Different instances of adata source may be provided by different vendors. For example, the sameorganization may store data in relational databases including instancesof ORACLE database, SQLSERVER, TERADATA, MYSQL, and so on. Otherexamples of data sources include data lakes, for example, data lakesoffered by CLOUDERA; files stored in a distributed file system, forexample, HDFS (HADOOP distributed file system; and cloud datawarehouses. A data source may be stored in a cloud based system, forexample, AWS (AMAZON web services), MICROSFT AZURE, and so on.

The third-party systems 120 provide additional information for example,data models to the analysis system. In an embodiment, a data modelrepresents a virtual data model generated from questions received fromusers. The analysis system 100 may store the virtual data modelsgenerated from questions and use them for processing subsequentquestions received from users. In an embodiment, a data model comprisesentities and relations between entities. Each entity may compriseattributes (also referred to herein as fields). Each entity represents aset of records. A data model is associated with a set of questions thatcan be answered using the data model. The questions may be naturallanguage questions or may be specified using a programming language. Thequestions can be translated into instructions for processing thequestion using the data model. For example, the instructions may useoperations such as filtering, selecting fields, joining entities usingrelations and so on. The data model may specify data types of thefields. The data model may specify additional information describing thefields, for example, data format of the fields. For example, a fieldstoring address information may be required to conform to certain dataformat used by standard addresses, a field storing date values mayconform to certain date formats, and so on.

The client device 130 used by a user for interacting with the analysissystem 100 can be a personal computer (PC), a desktop computer, a laptopcomputer, a notebook, a tablet PC executing an operating system, forexample, a Microsoft Windows®-compatible operating system (OS), Apple OSX®, and/or a Linux distribution. In another embodiment, the clientdevice 130 can be any device having computer functionality, such as apersonal digital assistant (PDA), mobile telephone, smartphone, wearabledevice, etc. The client device 130 may be used by a user to view resultsof analysis performed or for providing instructions to the analysissystem 100.

FIG. 1 and the other figures use like reference numerals to identifylike elements. A letter after a reference numeral, such as “110(a)”indicates that the text refers specifically to the element having thatparticular reference numeral. A reference numeral in the text without afollowing letter, such as “110,” refers to any or all of the elements inthe figures bearing that reference numeral (e.g. “110” in the textrefers to reference numerals “110(a)” and/or “110(n)” in the figures).

The interactions between the analysis system 100 and the other systemsshown in FIG. 1 are typically performed via a network, for example, viathe Internet. The network enables communications between the differentsystems. In one embodiment, the network uses standard communicationstechnologies and/or protocols. The data exchanged over the network canbe represented using technologies and/or formats including the hypertextmarkup language (HTML), the extensible markup language (XML), etc. Inaddition, all or some of links can be encrypted using conventionalencryption technologies such as secure sockets layer (SSL), transportlayer security (TLS), virtual private networks (VPNs), Internet Protocolsecurity (IPsec), etc. In another embodiment, the entities can usecustom and/or dedicated data communications technologies instead of, orin addition to, the ones described above. Depending upon the embodiment,the network can also include links to other networks such as theInternet.

System Architecture

FIG. 2 is the system architecture of an analysis system for performingdata model based analysis of data, in accordance with an embodiment. Theanalysis system 100 includes an external interface module 210, a datamodel store 215, a data store 220, a metadata store 225, and an analysismodule 230. Other embodiments may include fewer or more modules thanthose indicated herein. Functionality indicated herein as beingperformed by a particular module may be performed by other modulesinstead.

The external interface module 210 interfaces with external systems, forexample, data source systems 110, third party systems 120, and clientdevices 130. In particular, the external interface module 210 receivesconnection parameters for connecting with external systems, for example,data source systems 110. The external interface module 210 establishes aconnection with the data source system 110 to receive metadata or datafrom the data source system. The external interface module 210 storesmetadata received from data source systems 110 in the metadata store 225and data received from data source systems in data store 220. Theexternal interface module 210 establishes connections with thethird-party systems 120 to receive data models. The external interfacemodule 210 stores data models received from third-party systems 120 inthe data model store 215. The analysis module 230 also generates virtualdata models from questions asked by users and stores them in the datamodel store 215.

The analysis module 230 comprises other modules including the matchingmodule 235, match score module 240, question builder 245, pre-processingmodule 250, and a data quality assessment module 255. The actionsperformed by these modules and the processes executed by these modulesare further described herein.

The matching module 235 matches the metadata describing the data storedin the data source systems 120 against questions, for example, usingvirtual data models generated form questions to determine whether thedata source provides the required information.

The match score module 240 determines a match score for questions ordata models matched against data of one or more data sources. The matchscore module 240 matches the table names/file names or names ofcollections of data of the data source to determine whether a questionor data model matches data of the data source.

The question builder 245 allows users to ask natural language questionsthat the analysis system converts to query languages processed bysystems, for example, SQL (structured query language). The questionbuilder 245 receives requests from users to identify questions that canbe answered using one or more data sources and performs the processingnecessary to answer the questions.

The pre-processing module 250 determines quality of data that isavailable for answering a question or questions associated with a datamodel and determines whether preprocessing is required for improving thequality of the data. The pre-processing module 250 makes recommendationsfor portions of data that should be cleansed to improve the quality ofthe data to process a question.

The data quality assessment module 255 determines measures of dataquality of data sources to improve matching accuracy. In an embodiment,the data quality assessment module 255 measures data quality based onanalysis of values stored in a data source, for example, based on numberof null values stored in a column, number of format errors in valuesstored in a column, and so on. The data quality assessment module 255determines and provides metrics indicative of data quality to the matchscore module 240 and the match score module 240 uses the metricsreceived for determining match scores for data models or questions.

The different modules of the analysis system 100 may executeinstructions in response to commands executed by a user via a userinterface of the client device 130. For example, the analysis system 100may present a user interface via a client device 130 that allows a userto interact with the analysis system 100 to identify various questionsthat can be answered using the data sources used by an organization andexecute specific question.

Workflow Scenarios

FIGS. 3A-H illustrate the user interfaces for interacting with theanalysis system, in accordance with an embodiment. The user interfacesmay be presented by an application, for example, the internet browserexecuting on a client device 130 that interacts with the analysis system100.

FIG. 3A shows a user interface presenting a comprehensive view of alldata sources available in a system environment, according to anembodiment. The user provides information describing various datasources 305 in a system environment to the analysis system 100. Theinformation describing a data source includes the address for creating aconnection with the data source, for example, a network address or a URL(uniform resource locator). The information describing a data source mayinclude a port number for creating a connection with the data source.The information describing a data source may include credentialsrequired for creating a connection with the data source, for example, alogin identifier and a password or a security token that allows theanalysis system 100 to create a connection with the data source. Theanalysis system 100 may connect with different types of data sourcesincluding relational data management systems (RDBMS), HADOOP file system(HDFS), cloud based systems such as S3 file system, and so on.

Each data source comprises data assets that represent smaller units ofdata storage, for example, tables in an RDBMS, or files in a filesystem. The analysis system 100 connects with various data sources andretrieves metadata describing the data in each data source withoutretrieving data stored in the data assets. Examples of metadata includevarious fields in a table or various columns in a file. For example, ifa user table stores information describing specific users, the analysissystem 100 retrieves metadata such as various fields of the table butdoes not retrieve records describing specific users. The analysis system100 presents information describing data assets of a data source to theuser via a user interface that allows the user to select specific fieldsfor further analysis.

FIG. 3B shows a user interface for allowing a user to select specificdata assets to be processed in a data source, according to anembodiment. The user interface 308 displays various data assetscomprising tables of a database and is configured to receive selectionof a subset of the data assets (e.g., tables). The analysis system 100may store information describing the user selections and use theselected subset of data assets for further analysis.

In some embodiments, the analysis system 100 allows users to copy dataor files or tables to a sand box system separate from the data source.The analysis system 100 accesses the sand box system to access themetadata describing the copied data. This allows the data source toprovide the metadata to the analysis system 100 without providing accessto the data source. In an embodiment, the sand box system is provided byan entity associated with the data source, for example, an enterprisethat maintains the data source systems.

The analysis system 100 presents a user interface comprising a widget310 for receiving search requests from users. The search requestsreceived may comprise search keywords or search expressions. The searchrequest may be a natural language sentence such as a question. Theanalysis system 100 identifies various data assets or combinations ofdata assets that can be combined to answer the search request.

In some embodiments, the analysis system 100 connects to a data catalogof a data source to access the metadata. The metadata collected by theanalysis system 100 also includes names, tags, and synonyms defined bythe data source. A synonym refers to a new name defined for a dataasset, for example, an alias.

FIG. 3C shows the user interface for receiving a search request andpresenting data assets that match the search request, according to anembodiment. As shown in FIG. 3C several results 312 representing dataassets matching the search requests may be presented via the userinterface. The analysis system 100 performs the search across multipledata sources and identifies any data asset that matches the searchrequest including tables, columns, files, and so on. The user interfaceprovides the file/table/column name, a vendor name for the data source,a system name for the data source, a data type for the data asset, andlocation (URL) of the data source.

The analysis system 100 allows users to determine how a particular dataasset returned as a search result is being used by other users. Theanalysis system 100 provides a column 315 storing information describingassociated questions for each data asset. The user can interact with thefields of the column 315 to access questions associated with the column.Accordingly, the analysis system presents a user with a set of questionsassociated with a data asset representing the search result.

FIG. 3D shows the user interface for presenting associated questions fora search result, according to an embodiment. The associated questionsmay represent questions that were asked by other users for which thisdata asset was returned. The analysis system stores these questions andassociated execution plans and virtual data models. An associatedquestion is also referred to herein as a stored question since thequestion was previously asked by user and is stored by the analysissystem.

In an embodiment, the analysis system 100 tracks the search results thata user accessed to identify questions associated with a data asset. Forexample, if a data asset is returned as a result of a natural languagequestion and the user accesses the data asset returned in response tothe question, the analysis system associates that question with the dataasset. Similarly, if a data asset is returned as a result of a naturallanguage question and no user accesses the data asset returned inresponse to the question, the analysis system does not associate thatquestion with the data asset.

In an embodiment, the analysis system stores weights with eachassociation between a data asset and a question. For example, the weightof an associated question for a data asset may depend on the frequencywith which a data asset is accessed by users in response to thequestion. The weight of an associated question for a data asset maydepend on the amount of time that the data asset is accessed by users inresponse to the question. The higher the frequency with which a dataasset is accessed in response to a question, the higher the weight ofthe association between the question and the data asset as determined bythe analysis system 100. Similarly the higher the amount of time that adata asset is used by users, for example, for processing or for viewinginformation describing the data asset in a user interface, the higherthe weight of the association between the question and the data asset asdetermined by the analysis system 100.

In an embodiment, the analysis system 100 builds a virtual data modelrepresenting a question. The virtual data model may comprise one or moreentities and relations between the entities. FIG. 3E shows a userinterface presenting a virtual data model for a question 320, accordingto an embodiment. The virtual data model comprises entities 323 a, 323b, 323 c and relationships between the entities, for example,relationship 325 a between entities 323 a and 323 b and relationship 325b between entities 323 b and 323 c. The analysis system 100 furthergenerates and displays instructions 328 representing directions to thevarious systems for accessing the required data assets from theircorresponding data source to be able to answer the question 320. Theinstructions may specify how the various data assets should be combinedto generate the result, for example, whether two tables should bejoined, what columns to use for joining and the type of join (innerjoin, left outer join, right outer join, etc.)

The user interface further allows a user to inspect all possible dataassets that may correspond to an entity in the entity relationship graphrepresenting the virtual data model constructed for the question 320.FIG. 3F shows the user interface for allowing a user to modify the dataasset corresponding to an entity of the virtual data model correspondingto a natural language question, according to an embodiment. For example,the user interface 330 shows several data assets 335 a, 335 b, 335 c andso on corresponding to an entity from the virtual data model for aquestion 320. The user interface 330 allows a user to inspect thevarious data assets corresponding to an entity and select a particulardata asset. For example, the analysis system 100 may rank the dataassets based on their relevance and select a data asset by default.However, the user interface 330 allows the user to change the selecteddata asset and use a different data asset for that entity. The analysissystem 100 uses any modified data assets for answering the questionreceived from the user.

The analysis system 100 stores lineage of data assets representinginformation describing where the data stored in the data asset wasobtained from. The analysis system shows the lineage via a userinterface and allows users to inspect lineage of a data asset. FIG. 3Gshows a user interface for allowing a user to view lineage of data,according to an embodiment. In an embodiment, the user interface 340shows the lineage of a data asset as a data graph where each noderepresents either an action performed to obtain data, for example, aload operation or a data source such as a file or a table from where thedata was obtained. The lineage graph shown in the user interface allowsa user to track the original of a data, thereby determining whether thedata was obtained from a reliable source, for example, whether the datawas obtained from a source that conforms to certain privacy policy. Theanalysis system 100 allows a user to determine whether using data storedin a particular data asset will expose the user to certain liabilities,for example, if the data was obtained from a source that violatedcertain privacy policies.

Process for Accessing Data From Heterogeneous Data Sources

FIG. 4 is the overall process of collecting data used by the analysissystem, in accordance with an embodiment. The analysis system 100interacts with a plurality of heterogeneous data sources for processingquestions. The analysis system 100 receives 410 connection informationfor one or more data sources. The connection information may include anaddress and port number for establishing a connection with a data sourcesystem 110. The connection information may also include logininformation, for example, user name and password for connecting to adata source system.

The analysis system 100 repeats the steps 415, 420, and 425, for eachdata source. The analysis system 100 creates 415 a connection with thedata source system 110. The analysis system 100 receives 420 metadatadescribing the data stored in the data source 125 of the data sourcesystem 110 using the connection created with the data source system 110.The analysis system 100 stores 425 the accessed metadata in the metadatastore 225.

In an embodiment, the analysis system identifies what data matched andwhat did not match across table names, file names, field names, andmetadata values. The analysis system uses metadata to create a dynamicmapping to an entity relationship diagram based off of the data modelthat represents a live view of the data sources. The analysis systemcreates step-by-step directions of how to find the data, how to accessthe data and assemble the data to construct the data model.

The metadata received by the analysis system 100 may include names oftables, files, or any named unit of data storage of the data source. Thenamed units of data storage store collections of records or rows ofdata. The metadata further comprises fields for each table. A field of atable may also be referred to as a column of the table. The metadata mayfurther comprise information describing the data source, for example,data types of each field, statistics describing the table (e.g., size ofthe table), and so on. In an embodiment, the analysis system 100accesses samples of data from each table or file to extract additionalinformation describing the data.

The analysis system 100 receives questions, for example, naturallanguage questions from users. The analysis system 100 processes thequestions using the metadata obtained from the data sources. In anembodiment, the analysis system receives one or more data models fromone or more third party systems and stores the accessed data models inthe data model store 215. In an embodiment, the analysis system 100generates virtual data models based on the received questions and storesthe virtual data models in connection with the question corresponding tothe virtual data model. The analysis system 100 may compare a newquestion received from a user against the stored data models includingstored virtual data models of previous questions to determine if any ofthe stored data model either answers the new question or provides atleast a portion of answer to the new question, for example, a partialresult that can be further processed to answer the question.

Processing Natural Language Questions Based on Data Stored inHeterogeneous Data Sources

Embodiments allow a user to ask natural language questions and answerthe natural language questions by identifying specific data that isrequired to answer the question, identifying a subset of a plurality ofdata sources that provide the required data without actually fetchingthe data from the sources, generating instructions (e.g., statements,queries, or commands) to process combine the data, and executing theinstructions. The plurality of data sources may include data sourceshaving different architectures, for example, relational databasemanagement system (e.g., ORACLE or DB2), cloud based system (e.g.,Amazon Web Services or AWS), and parallel database systems (e.g.,TERADATA).

Accordingly, the input required from the user is limited to (1) thequestion that the user wants to ask and (2) the filters to specify whichsubset of data the user would like to process (e.g., recent data, datawithin a range of a particular column, and so on). Furthermore, thegenerated queries ensure that the data of each data source is processedby the data source and a partial result is fetched from each data sourceand aggregated by the analysis system. Since the analysis systemreceives only the subset of data representing the filtered data andprocesses the subset of data (which is small in size), the analysissystem can process the data in-memory and is highly efficient.

The analysis system sends the filters/limits (or range) corresponding toeach data source to the data source to execute a subquery at the datasource. The analysis system receives only the filtered data from eachdata source as a partial result. The analysis system joins the filtereddata to determine the aggregate result representing the answer to thequestion. The analysis system determines the keys to be used forperforming the joins and also generates the instructions for performingthe joins.

FIG. 5 shows a process of receiving and processing a natural languagequestion by generating a virtual data model, specific to the receivednatural language question, according to an embodiment. The analysissystem receives 510 a natural language question for accessing data thatmay be spread across a plurality of data sources. The analysis systemparses 520 the natural language question to determine various componentsof the question including the data elements that are referred to in thequestion and the intent of the question. The analysis system generates530 a virtual data model specific to the data fields identified asmatching the natural language question. The virtual data model comprisesentities that contain the data fields and relations between the dataentities. The analysis system identifies 535 data sources that match thegenerated virtual data model.

In an embodiment, the analysis system identifies multiple data sourcesand ranks them based on their relevance to the data model to select aparticular data model. The analysis system generates 540 a set ofinstructions for accessing and processing the necessary data to answerthe question. These set of instructions are also referred to herein asan execution plan for the question. The analysis system stores 545 thegenerated set of instructions in association with the question. Thesystem may execute the instructions to generate the answer and presentthe answer. Alternatively, the analysis system may send the instructionsfor review or for manual execution by a user, for example, a systemadministrator.

A user may specify filters to identify the subset of each type of datathat needs to be processed. The user may specify certain limits (upperand lower limits of rows), for example, a user may specify that thefirst 100 rows of the data should be used to answer the question. A usermay specify that rows with an attribute value within a specified rangemust be used to answer the question, for example, rows that representdata collected in the past one year. The user may specify that allrecords having an attribute value within a lower limit and an upperlimit need to be processed.

The analysis system determines a subquery for each data source and sendsthe corresponding subquery to each respective data source. This providesadditional resources to execute each subquery since the resources of thedata source are typically more powerful that the analysis system.Furthermore, if there is sensitive data in the data source, thesensitive data is processed at the source and not copied to the analysissystem.

The analysis system only receives a partial result obtained byprocessing the subqueries. For example, there may be data complianceissues at the data source. Fetching the data at the analysis systempropagates the compliance issues to the analysis system. furthermore, ifthe generated results are transmitted to another system, the complianceissues are transferred to that system. Therefore, the analysis systemminimizes copying of data from the source to minimize propagating anycompliance issues. In an embodiment, the analysis system processes anydata in memory and destroys the data after the processing withoutstoring the data in a persistent store to minimize data complianceissues. Furthermore, since only the partial results are copied from thedata sources to the analysis system and the partial results aretypically much smaller than the amount of data processed at each datasource, the transfer of data is fast.

In some embodiments, the question is based on a data model stored in theanalysis system. In other embodiments, the question is independent ofany data model. Accordingly, a user is able to ask questions and getanswers from data stored across a plurality of heterogeneous datasources. Accordingly, the analysis system receives a natural languagequestion and constructs on demand, a virtual data model (also referredto as a logical data model) representing the question asked.

A typical data model is designed to address questions related to aspecific topic. A data model identifies entities and relations betweenentities to be able to answer questions based on the entities and theirrelations. Each entity may have one or more fields. Different portionsof the data model may be used to answer different questions. Theanalysis system receives a question and determines a subset of the datamodel that is relevant to that particular question.

However, embodiments of the system allow users to ask a natural languagequestion without referring to a data model. The analysis system analyzesthe natural language question to generate a data model on the fly thatis suitable for asking that specific question. The system accesses andstores metadata describing data from various data sources. In anembodiment, the system stores various keywords identifying the type ofdata stored in each field. The analysis system identifies variousobjects referred to in the question. The analysis system matches theobjects referred to in the question with metadata of various datasources. In an embodiment, the analysis system analyzes the keywords toidentify the relevant fields. The analysis system determines attributesof the various keywords of the question to determine which keywordsrepresent filters. For example, the analysis system determines keywordsthat represent a quantity, keywords that represent a location, keywordsthat represent a noun/object, and so on. For example, if the analysissystem receives a question with keyword “when”, the analysis systemidentifies columns representing time in the table. If the analysissystem receives a question with keyword “where”, the analysis systemidentifies a column representing a location/place in the table. If theanalysis system receives a question with keyword “dollar”, the analysissystem identifies a column representing a revenue/profit in the table.

The analysis system further analyzes keywords in the natural languagequestion to determine the intent of the user in the question, i.e., thetype of information that the user is looking for via that question. Theanalysis system uses the derived intent to infer the relationshipbetween the various fields that are referred to in the question. In anembodiment, the system stores examples/types of data that is stored ineach field. If a natural language question refers to a particularkeyword that is likely to be present in that field, the analysis systemdetermines that field as being relevant to that keyword in the question.The analysis system may match fields based on format of data stored inthe data field and the format of a data value referred to in thequestion. For example, if the question uses the keyword “1980”, theanalysis system identifies a field that stores “year” in a table asbeing relevant for answering the question based on a match between thevalues stored in the “year” field and the value “1980” used in thequestion.

The analysis may match multiple data sources with each object from thequestion and may rank them in order of relevance to select the mostrelevant or most usable data source. For example, two data sources maystore values of a particular attribute. However, one data source maystore data in a manner that satisfies data compliance regulationswhereas another data source may not be complaint. In this situation, theanalysis system ranks the data from the compliance source higher.Similarly, one data source may store data that is better populatedwhereas the other data source may store mostly null values. In thisexample, the analysis system may rank the better populated data higher.

The analysis system furthermore determines relations between variousattributes identified in the question. For example, if the questionrefers to information that maps to two different attributes A1 and A2and A1 is stored in a data source D1 and A2 is stored in data source D2,the system analyzes metadata of the data sources D1 and D2 to determinethe tables T1 and T2 that store the respective attributes A1 and A2. Theanalysis system analyzes the metadata of the tables T1 and T2 todetermine the key columns required to join across the two tables T1 andT2. The analysis system generates queries to perform the required joins.The analysis system requests users to specify the filters for each tablerequired to answer the question. The system receives the filters fromthe user. The system generates subqueries that apply the relevant filterfor each table and computes the necessary subset of data from thattable. The analysis system sends the respective subqueries to each datasource. Each data source executes the respective subquery to generate apartial result set. The analysis system receives the results ofexecution of each subquery from the respective data source. The analysissystem executes a final query for combining the received partial resultto answer the question.

In an embodiment, the analysis system saves the questions in questionstore 265 to build a knowledge base of questions and their correspondinginstructions that can be executed for answering the question. Theknowledge base is used as a training data set for machine learning basedmodels. The knowledge based is also used for recommending questions thatcan be asked using a given set of data sources. A user can search withinthe question store 265 for relevant questions. The user can select anappropriate question and the analysis system accesses the instructionsfor answering the questions and executes them.

The system analyzes past queries and natural language questions to builda data store of relations between entities. For example, if a query waspreviously processed that joins two tables in a particular way, theanalysis system stores the relation between the two tables for use bysubsequently received natural language questions. The analysis systemalso stores statistics based on the results, for example, the number ofrows that were joined between two tables based on the question, numberof null values present in the data that was processed for the question,and so on. The analysis system uses the statistics to rank the entitiesfor subsequent question and determine which data sources should be usedand which relations should be exploited to process the subsequentquestions. Accordingly, statistics and analysis performed for pastqueries is used to determine relationships between fields and used forranking data sources and relations between data sources for use inanswering future natural language questions. The analysis system alsouses existing data models, for example, data models that are standardacross industries to determine relations between various fields of datasources that exist within and across data sources.

In some embodiments, the system recommends certain fields and relationsbetween fields for answering a question but allows a user to edit thefields/relations used. For example, a user may prefer to use afield/relation that is ranked low by the analysis system. The systemstores the user provided information. In an embodiment, the system usesthe user input as labelled data for training a machine learning basedmodel. The machine learning based model receives questions orinformation describing the question and predicts the fields andrelations of a data model corresponding to the question. In someembodiments, the model receives keywords and intents extracted from anatural language question as input and predicts the fields/relations touse for processing questions based on the fields/relations. The modelmay receive an input data field/relation and an input question andgenerate a score indicating a measure of relevance of the field/relationto the input question.

FIG. 6 illustrates the process of mapping data stored in data sources tonatural language question, in accordance with an embodiment. Theanalysis system 100 may repeat the following steps 605, 610, 615, 620multiple times. The analysis system 100 receives 605 a question, forexample, a natural language question. The question may also be a searchrequest or a search query using a structured query language. Theanalysis system generates 610 a virtual data model for the question. Thevirtual data model may represent entities identified in the questionsand relations between the entities based on the question. The analysissystem 100 maps the virtual data model to data assets of various datasources. The analysis system 100 repeats steps 615 and 620 for each datasource that stores data of the organization. The analysis system 100matches metadata of the data source with the virtual data model. Theanalysis system 100 stores the mapping from data elements of the datamodel to data elements of the data sources. In an embodiment, theanalysis system 100 stores instructions for answering the question usingdata assets of one or more data sources. Accordingly, each receivedquestion may result in a data model being generated and stored in theanalysis system 100. In an embodiment, the analysis system 100determines a usage score for each question indicating how many times thequestion is invoked, for example, as a result of a user asking the sameor very similar question or as a result of a user asking a question forwhich the stored question provides at least a portion of the answer.

The analysis system ranks the questions based on their usage score. Theanalysis system 100 uses the ranked order to determine which storedquestions to match first against a new question. The analysis system 100uses the ranked order to determine the order in which stored questionsmay be presented to a user in response to processing a new question, forexample, by asking the user if the new question can be answered byanswering any of a list of stored questions that match the new question.The analysis system 100 may prune a stored question and itscorresponding virtual models if the question has less than a thresholdusage score. This allows the analysis system 100 to manage storage andperformance of processing new questions by maintaining a reasonable setof stored questions.

The analysis system 100 performs matching of entity names with tablesnames for a data source that is a database (or file names for filesystems). If an entity name matches a table name, the analysis system100 matches the columns as well to determine whether the table storesall or some of the data required to represent the entity. If the entityname does not match any table name, the analysis system matches thefield names of the entity with column names of tables of the datasource. If more than a threshold number of fields of an entity match thecolumns of a table, the analysis system stores a mapping from the entityto the table.

The analysis system 100 may store multiple mappings for a single entityor a single field. For example, columns of multiple tables may store thevalues corresponding to a field of an entity. Similarly, differenttables or sets of tables may store values corresponding to an entity.The analysis system stores a match score indicating a degree of matchingbetween an entity and a table or a set of tables. For example, columnsof a table T1 may match 90% of the fields of the entity and columns of atable T2 may match only 60% of fields of that entity. Accordingly, tableT1 has a higher match score with the entity compared to table T2.

In an embodiment, the analysis system 100 performs matches based onother metadata attributes of tables, for example, data type of variousfields. In an embodiment, the analysis system 100 uses regularexpressions associated with fields of the data model to determinewhether a column in a data source matches the field of the data model.The analysis system 100 may perform match between elements of datasources and elements of a data model by sampling data from the datasource and determining characteristics of the data for matching. Forexample, the analysis system 100 may sample data to determinecharacteristics of the data to infer data types for fields. The analysissystem 100 uses the data types that may be specified in the metadata ofthe data sources or inferred from the sampled data to match elements ofdata model with elements of the data source. In an embodiment, theanalysis system determines a match score as a weighted aggregate ofscores indicative of matches of one or more of (1) table names andentity names (2) column names of data source with field names ofentities in a data model, (3) data types, (4) any other metadata, forexample, statistics describing the data elements.

In an embodiment, different entities of the data model may be mapped todifferent data sources, for example, entity E1 may be mapped to table T1of data source D1 and entity E2 may be mapped to table T2 of data sourceD2. In an embodiment, different fields of an entity may be mapped to twodifferent tables that may either belong to the same data source or totwo different data sources. For example, entity E may have a set S1 offields and a set S2 of fields. The set S1 of fields may be mapped tocolumns of table T1 and set S2 may be mapped to columns of table T2.Tables T1 and T2 may belong to the same data source or to two differentdata sources. In this example, the analysis system 100 identifies arelation between the two tables T1 and T2, for example, a foreignkey/primary key based relationship.

The analysis system 100 receives 625 a new question associated with thedata model. The analysis system 100 generates 630 an execution plan foranswering the question based on the mapping. The analysis system 100executes 635 the generated execution plan to determine a result set asan answer to the question. The analysis system 100 sends 640 the answerto the question, for example, to a client device that requested theanswer.

Process for Ranking Data Assets and Stored Questions Using Match Score

FIG. 7 is the process of ranking data assets for a question, accordingto an embodiment. The analysis system 100 receives 710 a question, forexample, a natural language question. The analysis system 100 maygenerate a virtual data model for the natural language question, whereinthe virtual data model comprises one or more entities and optionallyrelationships between entities. The analysis system 100 parses thequestions to identify keywords that are likely to correspond toentities. The analysis system 100 may determine that keywords thatrepresents objects in the natural language sentences, for example,subject and predicate are likely to match entities in a data model. Theanalysis system 100 matches 715 these keywords in the question with dataassets of various data sources.

The analysis system 100 repeats following steps 720, 725, and 730 foreach keyword K1 identified for matching against data assets. Theanalysis system 100 repeats the steps 720 and 725 for each data source.The analysis system 100 identifies 720 data assets matching the keywordK1 and determines a score for each matching data asset. For example, ifthe keyword is “employee”, the analysis system 100 may associate thekeyword with an entity Employee and identifies data assets that storeemployee data. The analysis system 100 may identify data assetscorresponding to the keyword, for example, by matching the keyword basedon matching of the name of the data asset, for example, name of thetable or file name. The analysis system 100 may identify data assetsmatching the entity corresponding to the keyword or matching the keywordbased on matching of the metadata, for example, description of the dataasset. The analysis system 100 may identify matching data assets basedon matching of the data stored in the data asset, for example, theformat of the data. For example, the format of data may be used todetermine that a column stores addresses or social security numbers, andso on.

The analysis system 100 determines 725 a match score for the matchingdata asset. The match score may be referred to as a score or as arelevance score for the data asset in relation to the received question.The analysis system 100 determines the match score for each data set inrelation to a received question based on several criteria. The analysissystem 100 determines a match score for a data asset based on theaggregate number of distinct questions previously asked that matched thedata asset and are stored in connection with the data asset.Accordingly, the match score for a data asset is directly proportionalto the number of distinct questions previously asked that matched thedata asset. In an embodiment, the analysis system 100 determines a matchscore for a data asset based on a weighted aggregate of the previouslyasked questions that matched the data asset, wherein the weight dependson a match score for the previously asked question. The weight may bebased on a degree of match between the previously asked question matchedthe received question indicating closely the previously asked questionmatches the received question.

In an embodiment, the match score of a data asset is weighted based onthe amount of data stored in the data asset. For example, a data assetthat has large amount of data is weighted higher than a data asset thathas less data. The two data assets may be from two different datasources. The match score for a data asset may be weighted based on thenumber of times the data asset is accessed, e.g., directly related to afrequency of access of the data asset. Accordingly, the match score forthe data asset is weighted high if the data asset is frequently accessedby users. In an embodiment, the match score for a data asset isdetermined based on a number of times the data asset is accessed afterbeing presented as a result in response to a previously asked question.Accordingly, the analysis system 100 determines a match score to be avalue that is directly related to the frequency of access of the dataasset after being presented as a result in response to a previouslyasked question. The analysis system 100 may determines a match score fora data asset as a weighted aggregate over all previously asked questionsassociated with the data asset, wherein each previously asked questionis weighted based on the frequency of access of the data asset inresponse to being presented as a result for that question.

In an embodiment, the analysis system determines a quality score for adata asset. The quality score may be obtained from the data source ordetermined by the analysis system by accessing a sample of data of thedata asset from the data source. The quality score of a data asset maybe directly related to the number of null values in the data asset or anumber of values that have formatting errors. A formatting error may bepresent if the data is expected to conform a to some format rule butfails to conform. For example, values of a column of a table may beexpected to satisfy a regular expression or another syntax but fail toconform. The quality score of a data asset is indicative of low qualityif the percentage of data values in the data asset that are null or haveformat errors is greater than a threshold value.

The analysis system 100 ranks 730 the data assets matching a particularkeyword based on their match score. The analysis system 100 presents 735the ranked list of matching data assets for each keyword via a userinterface. The analysis system 100 may select a subset of matching dataassets based on the score for presenting 735. The analysis system 100may select a default data asset to be used for each keyword, forexample, the data asset with the highest score. The analysis system 100presents a user interface to the user that allows the user to select aspecific data asset to be used for each keyword. Accordingly, theanalysis system 100 may receive a user selection of a data asset thatmay be different from the data asset selected by the analysis systembased on the score. The analysis system 100 determines an execution planfor the question based on the selections of data assets.

Processing New Questions Using Execution Plans of Stored Questions

FIG. 8 is the process of processing questions using previously processedquestions, in accordance with an embodiment. The analysis system takesnatural language questions and converts them to instructions inlanguages that can be processed by systems, for example, SQL (structuredquery language). The analysis system 100 receives 810 a question thatmay be a natural language question or a search request with one or moresearch keywords. The analysis system 100 stores previously receivedquestions including natural language questions, structured languagequeries, and keyword based searches. These previously received andanswered questions are referred to as the stored questions. The analysissystem 100 identifies 815 a set of stored questions matching thereceived question.

In an embodiment, the analysis system 100 determines a match score foreach stored question that is determined to match the received question.For each matching question, the analysis system determines 820 a matchscore based on number of fields/metadata/files/tables associated withthe received question that match the fields/metadata/files/tables of thestored question. In an embodiment, the analysis system 100 performssemantic match of two input questions by comparing the natural languagetext of the two questions using a trained machine learning model, forexample, using a neural network trained to received two natural languagesentences as input and output a score indicating a degree of semanticmatch between the two input sentences. In an embodiment, the analysissystem 100 matches two questions by generating a virtual data model foreach input question and matching the two generated virtual data models.The virtual data models comprise an entity relationship graph and theanalysis system 100 matches two virtual data models by matching theircorresponding entity relationship graphs. In an embodiment, the analysissystem 100 weighs each question based on the number of times thequestion was invoked in the past. A question Q1 is invoked if a userasks the question Q1 directly, or if a user asks a question Q2 thatmatches the question Q1, thereby executing the execution plan of thequestion Q1, or if a user asks a question Q3 such that the executionplan of question Q1 is executed to generate at least a portion of theanswer of the question Q3.

In an embodiment, the analysis system ignores certain values of thenatural language questions when comparing two natural languagequestions. The analysis system 100 determines values of the naturallanguage question that correspond to parameters in the generated virtualdata model. For example, analysis system 100 determines that certainvalues mentioned in the natural language question map to parametersspecifying a range in a question. The analysis system 100 ignores thesevalues while comparing the natural language questions. This is sobecause the analysis system 100 reuses the execution plan of a storedquestion by replacing the parameter values of the stored question withthe parameter values of the received question. This allows the analysissystem 100 to reuse the effort previously spent in analyzing the storedquestion and generating the execution plan for the stored question.

The analysis system 100 ranks 825 the matching stored questions based onthe match score. The analysis system sends 830 for display via a UI, oneor more questions based on the ranking. In an embodiment, the analysissystem 100 selects the highest-ranking question as the matching questionand recommends it as the stored question that can be executed instead ofthe received question. If the user approves the recommendation, theanalysis system 100 executes the approved stored question instead of thereceived question. In an embodiment, the analysis system 100 presentsthe execution plan of the matching stored question to the user and letsthe user modify it before execution. For example, the user may modifythe values of a range or values of certain parameters before execution.

In an embodiment, the analysis system 100 presents a user interface thatpresents a list of matching stored questions as the user inputs the newquestion. For example, the list of matching stored questions ispresented in a drop-down list. The user may select a matching storedquestion without typing the entire new question. The analysis system 100receives 835 the user selection and presents the stored question in auser interface. The user interface is configured to allow the user tomodify the stored question, for example, to change certain parameters.The analysis system 100 receives the modified question and updates theexecution plan based on the modifications to the question. For example,the analysis system 100 replaces the parameter values of the executionplan obtained from the stored question with the corresponding parametervalues of the execution plan from the modified question.

In an embodiment, the analysis system 100 generates 840 a set ofinstructions for processing the data from the data sources to answer thequestions. The analysis system 100 sends 845 the set of instructionseither for display (e.g., via email), or for execution. The instructionsmay include: (1) instructions to extract data from a data source, (2)instructions to transform data to match formats across data sources, or(3) instructions to combine (join) data from different data sources togenerate new tables.

Process for Selective Preprocessing of Data

FIG. 9 is the process of performing preprocessing of data, in accordancewith an embodiment. The analysis system determines what data needs to bediscovered, moved, assembled, cleaned, based on a question or a datamodel, for example, a virtual data model generated from a naturallanguage question.

The analysis system receives 910 one or more questions. A question maybe a natural language question based on data stored in one or more datasources. The analysis system repeats the steps 915, 920, and 925 foreach question. The analysis system determines 915 an execution plan forthe question. The analysis system 100 identifies 920 a set offields/metadata/files/tables from the plurality of data sources that areprocessed by the execution plan. The analysis system 100 determines 925a measure of data quality of each field from the set. The analysissystem 100 may obtain the measure of data quality from the data sourcesystem 110 comprising the data source.

In an embodiment, the analysis system 100 provides instructions to thedata source system 110 to determine the data source quality. Theinstructions may request specific processing of each data asset togenerate measures of quality, for example, the instructions maydetermine a percentage of null or empty values in a column. Theinstructions may provide regular expressions for determining apercentage of values of a column, set of columns, or a table that do notconform to the regular expression. The instructions may provide anyother criteria for determining a percentage of values of a column, setof columns, or a table that do not conform to the specified criteria.The analysis system 100 may identify 930 specific data assets of thedata source and request data quality score for each specific data asset.In an embodiment, the data quality score is determined by accessing asample of data of one or more data assets and analyzing the accesseddata. In an embodiment, the data quality score is determined byaccessing all the data of one or more data assets from the data sourceand analyzing the accessed data.

The data quality of a data asset may be determined based on differentfactors. In an embodiment, the data quality measures the frequency ofoccurrence of null values in the data asset, for example, percentage ofdata elements of a column or a table that have null values. In anembodiment, the data quality measures the frequency of occurrence ofrecords or values in the data asset that fail to conform to certainformatting rules, for example, percentage of data elements of a columnor a table that have incorrect data format. The expected format for arecord or column may be specified using certain rules, for example,using regular expressions, context free grammars, or a function or setof instructions for checking the format. In an embodiment, the dataquality measures the frequency of occurrence of records or values in thedata asset that fail to conform to certain policy, for example, privacypolicy. A policy may specify that the data of certain type must bestored using certain type of encryption or masking. The policy mayspecify that data of certain type may not be made available to users andmust be protected by specific access control policies. In an embodiment,the analysis system 100 determines data quality based on lineage of thedata by identifying the origin of the data. For example, the analysissystem 100 determines data quality for a data asset to be low if thedata stored in the data asset was generated from data obtained from adata source that violated certain policies, for example, privacy policy.

The analysis system 100 identifies 930 a subset of the total set offields being processed for cleansing. The subset is identified based onthe measure of quality, for example, by selecting fields determined tohave low quality of data for preprocessing. In an embodiment, each fieldmay be associated with a cost of cleansing the data. The analysis systemselects fields based on their cost of cleaning the data along with thequality of the data. For example, the analysis system gives higherpreference to fields that are cheaper to clean. The analysis systemsends 935 information describing the identified subset of fields forpreprocessing, i.e., cleansing of the data by taking measures that willincrease the data quality.

If the analysis system 100 identifies a data asset that has a dataquality score that is below a threshold, thereby indicating low dataquality, the analysis system 100 sends information describing thereasons why the data quality is low. For example, the analysis system100 may specify one or more data format rules that were violated by adata asset. The analysis system may send messages or alerts. Theanalysis system may send a report describing any non-compliant fieldsand identify the data format rules that were violated by thenon-compliant fields. For example, the analysis system 100 provides thedata source with the rules or policies that the data asset violates. Theanalysis system 100 may provide the data source with a regularexpression if the elements of the data asset from the data source aredetermined to fail to conform to the regular expression.

In an embodiment, the data quality score indicates a level of complianceto any type of policy, for example, GDPR (General Data ProtectionRegulation) compliance or compliance to any privacy laws. If theanalysis system 100 determines that the data asset has low data qualitydue to the lineage of the data asset including a data source that actsas an origin of data and the data source fails to conform to certainpolicy, the analysis system 100 provides information describing thelineage and information identifying the data source that fails toconform to the policy as well as information identifying the policy.This allows the users or system administrators to fix the data asset,for example, by regenerating the data of the data asset using data froma different data source that conforms to the policy and therebymodifying the lineage of the data asset. Cleansing of data may requiredeveloping transformations from the bad data to generate clean data. Forexample, a transformation may modify addresses having non-standardformat to addresses having a standard format by rearranging variousfields of the address.

The analysis system 100 receives indication that the data asset has beenmodified to improve the data quality. The analysis system 100 mayreevaluate the data asset to measure its data quality score anddetermine whether the data quality score indicates an acceptable dataquality. If the data quality score still indicates low data quality, theanalysis system 100 continues to provide the data quality information totheir respective data sources with information describing the reasonsfor low data quality, thereby allowing the data source systems toperform data transformations of changes that improve the data quality.

Responsive to cleansing of the data, the analysis system 100 may processthe execution plan to obtain results for the question. In an embodiment,the analysis system considers multiple execution plans, for example, ifthe same data can be obtained from multiple sources or if there aremultiple ways to combine data for a particular data model. The analysissystem 100 evaluates different execution plans to identify the bestexecution plan, e.g., the execution plan that uses GDPR compliant datasources, or execution plan that requires minimum cleansing effort.

Execution of Questions Processing Data Across Multiple Data Sources

The analysis system may combine data across multiple data sources toanswer questions. In an embodiment, the analysis system 100 performs aquick join process to join tables of data sources. The analysis systemanswers the question by accessing the metadata for the names of thetables/files/fields as well as the primary keys and foreign keys toperform the join and leave the remaining data at the data source. Theanalysis system therefore does not move the data or aggregate the datato determine a result for the question. The analysis system federatesqueries across multiple data sources with the data remaining in eachdata source. The analysis system performs data isolation so as to avoidretrieving data that may violate certain polices and combining it withother data that does not violate such policies. The data isolationensures that the data that does not violate policies is kept separatefrom the data that violates policies. If the analysis system is requiredto combine such data, the analysis system tracks lineage of the data sothat if necessary, data that violates certain policies can be identifiedand replaced with equivalent data from another data source that does notviolate these policies. The analysis system is able to reuse all thescripts or instructions generated for performing such processing withthe data from the new data source.

In an embodiment, the analysis system pulls in only the primary keys andforeign keys of tables to perform the join and leaves the remainingcolumns at the data source. As a result, if certain columns do notcomply with certain policies (for example, if the data violates GDPR,i.e., General Data Protection Regulation), the analysis system processesthe query without fetching these columns.

The analysis system 100 receives a question that can be answered byprocessing a plurality of tables stored in one or more data sources. Thequestion returns a first set of columns as answer. However, the questionrequires performing a join based on a second set of columns. The secondset of columns ma include primary keys and foreign keys of tables. Theanalysis system retrieves only the second set of columns comprisingprimary/foreign keys from the one or more tables. The analysis systemleaves the first set of columns in their respective data sources anddoes not fetch them. Accordingly, the first set of columns is neverretrieved and stored in the analysis system. The analysis system 100performs the join operation using only the second set of columns. Theanalysis system 100 does not retrieve the first set of columns since itmay not comply with some policies/rules, e.g., not GDPR compliant. Theanalysis system 100 sends the result identifying the rows of the one ormore tables to a client device. The client device may retrieve the firstset of columns from the one or more data sources directly. As a result,the client device may obtain the full set of columns that represent theanswer to the question but the analysis system 100 does not fetch thefull set of columns. In particular, the analysis system does not fetchthe first set of columns.

In an embodiment, the analysis system receives access to a stagingsystem where the analysis system can store data temporarily forprocessing. The staging system represents a customer's bucket where datacan be stored. As a result, the analysis system never receives any datathat may be non-compliant. All data used by the analysis system forprocessing is stored in either the data sources of the data source,e.g., systems of the enterprise providing the data or in a staging areathat is provided by the enterprise that requested the analysis. Thisincludes data stored temporarily for processing.

Alternative Embodiments

In some embodiments, the analysis system allows selective data to beimported from a data source to perform processing and allows data to beexported to a data source. In an embodiment, the user interface used bya client device is an internet based browser that interacts with theanalysis system using web protocols such as HTTP.

In some embodiments, the analysis system computes and stores entities ortables based on existing data. The analysis system determines lineage ofeach entity or table that exists in the system identifying the sourcefrom which the entity or table was obtained. The analysis system tracksthe lineage for answers to a question, data of a data model, data of anentity, or data of a table. In an embodiment, the analysis system tracksthe execution plan of questions to determine the source of data used forcomputing the values. The lineage of data allows the analysis system todetermine quality of each data based on the quality of the sources usedto obtain the data. The quality of the data may be indicative of howclean the data is in terms of nulls/format errors/data type issues andso on. The quality of the data may be indicative of a degree ofcompliance of the data, for example, compliance with privacy rules. Inan embodiment, the analysis system determines a quality score for a dataas a weighted aggregate of the quality score of the individual inputdata elements (e.g., tables, fields, and so on) used to compute thedata.

Additional Considerations

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium or ina transmission signal) or hardware modules. A hardware module istangible unit capable of performing certain operations and may beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client or server computersystem) or one or more hardware modules of a computer system (e.g., aprocessor or a group of processors) may be configured by software (e.g.,an application or application portion) as a hardware module thatoperates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where thehardware modules comprise a general-purpose processor configured usingsoftware, the general-purpose processor may be configured as respectivedifferent hardware modules at different times. Software may accordinglyconfigure a processor, for example, to constitute a particular hardwaremodule at one instance of time and to constitute a different hardwaremodule at a different instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or processors or processor-implementedhardware modules. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but deployed across a number of machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environmentor as a server farm), while in other embodiments the processors may bedistributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs).)

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some portions of this specification are presented in terms of algorithmsor symbolic representations of operations on data stored as bits orbinary digital signals within a machine memory (e.g., a computermemory). These algorithms or symbolic representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Asused herein, an “algorithm” is a self-consistent sequence of operationsor similar processing leading to a desired result. In this context,algorithms and operations involve physical manipulation of physicalquantities. Typically, but not necessarily, such quantities may take theform of electrical, magnetic, or optical signals capable of beingstored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to these signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. It should be understood thatthese terms are not intended as synonyms for each other. For example,some embodiments may be described using the term “connected” to indicatethat two or more elements are in direct physical or electrical contactwith each other. In another example, some embodiments may be describedusing the term “coupled” to indicate that two or more elements are indirect physical or electrical contact. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still cooperate or interact with each other. Theembodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for asystem and a process for creating virtual databases from point-in-timecopies of production databases stored in a storage manager. Thus, whileparticular embodiments and applications have been illustrated anddescribed, it is to be understood that the disclosed embodiments are notlimited to the precise construction and components disclosed herein.Various modifications, changes and variations, which will be apparent tothose skilled in the art, may be made in the arrangement, operation anddetails of the method and apparatus disclosed herein without departingfrom the spirit and scope defined in the appended claims.

I claim:
 1. A computer-implemented method comprising: receivinginformation identifying a plurality of data sources; for each of theplurality of data sources, retrieving metadata describing the datastored in the data source; receiving a natural language question;identifying one or more phrases from the natural language question; andfor each of the one or more phrases: identifying from the plurality ofdata sources, a plurality of data assets matching the phrase, each dataasset determined to store data relevant for answering the naturallanguage question; determining a score for each of the plurality of dataassets, wherein a data asset is associated with a set of storedquestions, the score for the data asset determined based on a weightedaggregate of the stored questions for the data asset, wherein a storedquestion is weighted based on a number of times the stored question wasinvoked; ranking the plurality of data assets based on their scores; andsending for display via a client device, data describing at least asubset of the plurality of data assets, the subset determined based onthe ranking.
 2. The computer-implemented method of claim 1, wherein thescore for the data asset is determined based on a number of distinctstored questions for the data asset.
 3. The computer-implemented methodof claim 1, wherein each stored question is further weighted based on adegree of match between the stored question and the natural languagequestion.
 4. The computer-implemented method of claim 1, wherein each ofthe one or more phrases is associated with an entity and the score forthe data asset is based on a number of field names of the entity thatmatch fields of the data asset.
 5. The computer-implemented method ofclaim 4, wherein the score for the data asset is based on matching ofdata types of fields of the data asset and corresponding data types offields of the entity.
 6. The computer-implemented method of claim 1,wherein the score for the data asset is based on an amount of datastored in the data asset.
 7. The computer-implemented method of claim 1,wherein the score for the data asset is based on a measure of dataquality of data stored in the data asset.
 8. The computer-implementedmethod of claim 7, wherein the measure of data quality of data stored inthe data asset is based on a frequency of occurrence of nulls in fieldsof the data asset.
 9. The computer-implemented method of claim 8,wherein the measure of data quality of data stored in the data asset isinversely related to the frequency of occurrence of nulls in fields ofthe data asset.
 10. The computer-implemented method of claim 7, whereinthe measure of data quality of data stored in the data asset is based ona frequency of occurrence of fields that fail to conform to apredetermined format.
 11. The computer-implemented method of claim 10,wherein the measure of data quality of data stored in the data asset isinversely related to the frequency of occurrence of fields that fail toconform to a predetermined format.
 12. The computer-implemented methodof claim 1, wherein the score for the data asset is determined based ona degree of match between the phrase and metadata describing the dataasset.
 13. A non-transitory computer-readable storage medium storingcomputer-executable instructions for executing on a computer processor,the instructions when executed by the computer processor cause thecomputer processor to perform steps comprising: receiving informationidentifying a plurality of data sources; for each of the plurality ofdata sources, retrieving metadata describing the data stored in the datasource; receiving a natural language question; identifying one or morephrases from the natural language question; and for each of the one ormore phrases: identifying from the plurality of data sources, aplurality of data assets matching the phrase, each data asset determinedto store data relevant for answering the natural language question;determining a score for each of the plurality of data assets, wherein adata asset is associated with a set of stored questions, the score forthe data asset determined based on a weighted aggregate of the storedquestions for the data asset, wherein a stored question is weightedbased on a number of times the stored question was invoked; ranking theplurality of data assets based on their scores; and sending for displayvia a client device, data describing at least a subset of the pluralityof data assets, the subset determined based on the ranking.
 14. Thenon-transitory computer-readable storage medium of claim 13, wherein thescore for the data asset is determined based on a number of distinctstored questions for the data asset.
 15. The non-transitorycomputer-readable storage medium of claim 13, wherein each phrase isassociated with an entity and the score for the data asset is based on anumber of field names of the entity that match fields of the data asset.16. The non-transitory computer-readable storage medium of claim 13,wherein the score for the data asset is based on an amount of datastored in the data asset.
 17. The non-transitory computer-readablestorage medium of claim 13, wherein the score for the data asset isbased on a measure of data quality of data stored in the data asset. 18.The non-transitory computer-readable storage medium of claim 17, whereinthe measure of data quality of data stored in the data asset is based ona frequency of occurrence of fields that fail to conform to apredetermined format.
 19. The non-transitory computer-readable storagemedium of claim 13, wherein the score for the data asset is determinedbased on a degree of match between the phrase and metadata describingthe data asset.
 20. A computer-implemented system comprising: a computerprocessor; and a non-transitory computer-readable storage medium storingcomputer-executable instructions for executing on a computer processor,the instructions when executed by the computer processor cause thecomputer processor to perform steps comprising: receiving informationidentifying a plurality of data sources; for each of the plurality ofdata sources, retrieving metadata describing the data stored in the datasource; receiving a natural language question; identifying one or morephrases from the natural language question; and for each of the one ormore phrases: identifying from the plurality of data sources, aplurality of data assets matching the phrase, each data asset determinedto store data relevant for answering the natural language question;determining a score for each of the plurality of data assets, wherein adata asset is associated with a set of stored questions, the score forthe data asset determined based on a weighted aggregate of the storedquestions for the data asset, wherein a stored question is weightedbased on a number of times the stored question was invoked; ranking theplurality of data assets based on their scores; and sending for displayvia a client device, data describing at least a subset of the pluralityof data assets, the subset determined based on the ranking.
 21. Thecomputer-implemented system of claim 20, wherein the score for the dataasset is determined based on a number of distinct stored questions forthe data asset.
 22. The computer-implemented system of claim 20, whereineach phrase is associated with an entity and the score for the dataasset is based on a number of field names of the entity that matchfields of the data asset.
 23. The computer-implemented system of claim20, wherein the score for the data asset is based on an amount of datastored in the data asset.
 24. The computer-implemented system of claim20, wherein the score for the data asset is based on a measure of dataquality of data stored in the data asset.
 25. The computer-implementedsystem of claim 24, wherein the measure of data quality of data storedin the data asset is based on a frequency of occurrence of fields thatfail to conform to a predetermined format.